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DOC: Adapt some colors in examples #21226

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Oct 1, 2021
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DOC: Adapt some colors in examples #21226

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45 changes: 24 additions & 21 deletions examples/lines_bars_and_markers/fill_between_alpha.py
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Original file line number Diff line number Diff line change
Expand Up @@ -17,9 +17,6 @@
import matplotlib.cbook as cbook


# Fixing random state for reproducibility
np.random.seed(19680801)

# load up some sample financial data
r = (cbook.get_sample_data('goog.npz', np_load=True)['price_data']
.view(np.recarray))
Expand All @@ -29,7 +26,7 @@
pricemin = r.close.min()

ax1.plot(r.date, r.close, lw=2)
ax2.fill_between(r.date, pricemin, r.close, facecolor='blue', alpha=0.5)
ax2.fill_between(r.date, pricemin, r.close, alpha=0.7)

for ax in ax1, ax2:
ax.grid(True)
Expand All @@ -52,16 +49,19 @@
#
# Our next example computes two populations of random walkers with a
# different mean and standard deviation of the normal distributions from
# which the steps are drawn. We use shared regions to plot +/- one
# which the steps are drawn. We use filled regions to plot +/- one
# standard deviation of the mean position of the population. Here the
# alpha channel is useful, not just aesthetic.

# Fixing random state for reproducibility
np.random.seed(19680801)

Nsteps, Nwalkers = 100, 250
t = np.arange(Nsteps)

# an (Nsteps x Nwalkers) array of random walk steps
S1 = 0.002 + 0.01*np.random.randn(Nsteps, Nwalkers)
S2 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers)
S1 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers)
S2 = 0.002 + 0.01*np.random.randn(Nsteps, Nwalkers)
Comment on lines +63 to +64
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Note: Exchanging the two distributions results in a nicer image: S2 is drawn on top of S1. Drawing the artist with the smaller area on top looks better. One could achive the same effect by rearranging everything below, but since the data are arbitrary anyway, this change here is easiest.


# an (Nsteps x Nwalkers) array of random walker positions
X1 = S1.cumsum(axis=0)
Expand All @@ -77,10 +77,10 @@

# plot it!
fig, ax = plt.subplots(1)
ax.plot(t, mu1, lw=2, label='mean population 1', color='blue')
ax.plot(t, mu2, lw=2, label='mean population 2', color='yellow')
ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='blue', alpha=0.5)
ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='yellow', alpha=0.5)
ax.plot(t, mu1, lw=2, label='mean population 1')
ax.plot(t, mu2, lw=2, label='mean population 2')
ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='C0', alpha=0.4)
ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='C1', alpha=0.4)
ax.set_title(r'random walkers empirical $\mu$ and $\pm \sigma$ interval')
ax.legend(loc='upper left')
ax.set_xlabel('num steps')
Expand All @@ -93,11 +93,14 @@
# as the x, ymin and ymax arguments, and only fills in the region where
# the boolean mask is True. In the example below, we simulate a single
# random walker and compute the analytic mean and standard deviation of
# the population positions. The population mean is shown as the black
# dashed line, and the plus/minus one sigma deviation from the mean is
# shown as the yellow filled region. We use the where mask
# ``X > upper_bound`` to find the region where the walker is above the one
# sigma boundary, and shade that region blue.
# the population positions. The population mean is shown as the dashed
# line, and the plus/minus one sigma deviation from the mean is shown
# as the filled region. We use the where mask ``X > upper_bound`` to
# find the region where the walker is outside the one sigma boundary,
# and shade that region red.

# Fixing random state for reproducibility
np.random.seed(1)

Nsteps = 500
t = np.arange(Nsteps)
Expand All @@ -114,16 +117,16 @@
upper_bound = mu*t + sigma*np.sqrt(t)

fig, ax = plt.subplots(1)
ax.plot(t, X, lw=2, label='walker position', color='blue')
ax.plot(t, mu*t, lw=1, label='population mean', color='black', ls='--')
ax.fill_between(t, lower_bound, upper_bound, facecolor='yellow', alpha=0.5,
ax.plot(t, X, lw=2, label='walker position')
ax.plot(t, mu*t, lw=1, label='population mean', color='C0', ls='--')
ax.fill_between(t, lower_bound, upper_bound, facecolor='C0', alpha=0.4,
label='1 sigma range')
ax.legend(loc='upper left')

# here we use the where argument to only fill the region where the
# walker is above the population 1 sigma boundary
ax.fill_between(t, upper_bound, X, where=X > upper_bound, facecolor='blue',
alpha=0.5)
ax.fill_between(t, upper_bound, X, where=X > upper_bound, fc='red', alpha=0.4)
ax.fill_between(t, lower_bound, X, where=X < lower_bound, fc='red', alpha=0.4)
ax.set_xlabel('num steps')
ax.set_ylabel('position')
ax.grid()
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1 change: 1 addition & 0 deletions examples/lines_bars_and_markers/filled_step.py
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Expand Up @@ -53,6 +53,7 @@ def filled_hist(ax, edges, values, bottoms=None, orientation='v',
"not {o}".format(o=orientation))

kwargs.setdefault('step', 'post')
kwargs.setdefault('alpha', 0.7)
edges = np.asarray(edges)
values = np.asarray(values)
if len(edges) - 1 != len(values):
Expand Down
4 changes: 2 additions & 2 deletions examples/lines_bars_and_markers/gradient_bar.py
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Expand Up @@ -70,8 +70,8 @@ def gradient_bar(ax, x, y, width=0.5, bottom=0):
ax.set(xlim=xlim, ylim=ylim, autoscale_on=False)

# background image
gradient_image(ax, direction=0, extent=(0, 1, 0, 1), transform=ax.transAxes,
cmap=plt.cm.Oranges, cmap_range=(0.1, 0.6))
gradient_image(ax, direction=1, extent=(0, 1, 0, 1), transform=ax.transAxes,
cmap=plt.cm.RdYlGn, cmap_range=(0.2, 0.8), alpha=0.5)

N = 10
x = np.arange(N) + 0.15
Expand Down
2 changes: 1 addition & 1 deletion examples/lines_bars_and_markers/stackplot_demo.py
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Expand Up @@ -29,7 +29,7 @@

fig, ax = plt.subplots()
ax.stackplot(year, population_by_continent.values(),
labels=population_by_continent.keys())
labels=population_by_continent.keys(), alpha=0.8)
ax.legend(loc='upper left')
ax.set_title('World population')
ax.set_xlabel('Year')
Expand Down